PanGraphRNA: An efficient and flexible bioinformatics platform for graph pangenome-based RNA-seq data analysis.

Introduction: Colon cancer is the fourth most common cancer in the United States and the third leading cause of cancer-related death. High throughput genomic sequencing has led to a number of significant advances in tumor biology and in identifying novel signaling molecules such as long non-coding RNA (lncRNA). The aim of this study was to identify differentially expressed lncRNA in colon cancer from a large RNA-sequencing (RNA-seq) data set. Methods: The raw RNA-seq files of 398 patients with colon cancer were downloaded from The Cancer Genome Atlas (TCGA). Sequencing files were aligned using STAR (Spliced Transcripts Alignment to a Reference) to the most recent genome annotation. Using a subset of patients with paired colon cancer and normal colon epithelium RNA-seq data (n=40), an exploratory binomial regression model was used to calculate differential RNA expression. The most differentially expressed lncRNA were identified from the exploratory analysis and verified by comparing the larger colon cancer RNA-seq data set (n=358) with the normal colon epithelium RNA-seq data set (n=40). Results: 33,514 genes were identified from the comparative analysis, and using differential expression cut off values of > +1.5 or < -1.5 log fold change and a false discovery rate of <0.05; 543 were upregulated and 1822 were downregulated. Within this dysregulated group, 60 lncRNAs were identified. Using the larger data set (n=358 vs n=40), 41/60 lncRNA remained differentially expressed, of which 15 were downregulated and 26 were upregulated. Twenty-four of these lncRNAs have not previously been described in colon cancer, 12 of which are upregulated and 12 of which are downregulated (Table 1). Conclusions: This analysis of RNA-seq data from TCGA has identified dysregulated lncRNAs which have not previously been described in human colon cancer. These lncRNAs may have significant roles in colon cancer tumor signaling. Table 1.Differential expression of lncRNA not described in colon cancerlncRNALog Fold ChangeP-valueDescription in other cancersKRT16P14.1041.36E-21Increased in lung squamous cell carcinomaBOK-AS13.7784.98E-09Increased in oral squamous cell carcinomaCELP3.1151.30E-09Not availableCLDN10-AS12.3944.61E-12Not availablePOU6F2-AS12.2678.25E-09Not availableSLC7A11-AS12.1931.60E-13Decreased in gastric adenocarcinomaCSAG42.1083.59E-10Not availableLUCAT12.0224.21E-11Increased in Ovarian cancer, renal clear cell carcinoma, head and neck squamous cell carcinoma, Non-small cell cancer, glioma, osteosarcoma, esophageal squamous cell carcinomaVPS9D1-AS11.9795.27E-21Decreased in gastric adenocarcinoma, Increased in non-small cell lung cancerLEF1-AS11.8464.48E-13Increased in glioblastomaCASC81.6485.64E-06Single nucleotide polymorphisms in colorectal adenocarcinomaLINC004911.5903.11E-04Not availableLINC00648-1.5021.02E-15Not availableLINC00163-1.6751.03E-22Not availableSATB2-AS1-1.8074.18E-09Increased in osteosarcomaLINC00702-2.1424.81E-18Not availableTRHDE-AS1-2.2623.44E-24Not availableLINC00908-2.3082.76E-103Not availableLINC00483-2.4012.87E-39Increased in gastric adenocarcinomaLINC00461-2.4401.42E-41Increased in glioma, multiple myelomaFENDRR-2.6872.12E-58Decreased in breast cancer, prostate cancer, osteosarcoma, gastric adenocarcinoma,ARHGEF26-AS1-2.7148.55E-32Not availableADAMTS9-AS2-2.9041.78E-74Increased in lung cancer salivary adenoid cystic carcinoma, Decreased in glioma, gastric adenocarcinomaMT1JP-4.4601.08E-114Decreased in retinoblastoma, Decreased in gastric adenocarcinoma Citation Format: Stephen J. O'Brien, Theodore Kalbfleisch, Sudhir Srivastava, Shesh Rai, Susan Galandiuk. Differential expression of long non-coding RNA in colon adenocarcinoma RNA-sequence data set [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1817.

BackgroundRecently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.ResultsIn this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results.ConclusionSpearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0778-7) contains supplementary material, which is available to authorized users.

A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and in silico methods

The vast majority of human genes express more than one transcript isoform, and aberrant splicing patterns or changes in isoform expression ratios can lead to disease, notably cancer. Next generation sequencing platforms deliver the finest resolution yet of the transcriptome, and the wealth of sequencing data produced by TCGA provides an excellent basis for a broad study of alternative splicing in cancer. We have developed an interactive, exceptionally user-friendly tool, SpliceSeq, for investigation of alternative splicing in RNASeq data. The program is a follow-up to our SpliceCenter package (http://projects.insilico.us/SpliceCenter/SpliceOverview.jsp), which provides tools for use by the bench biologist in assessing experimental observations that may result from splice variation. Our results from SpliceSeq for TCGA RNASeq data from several cancer types will be presented. We identify pervasive changes in splicing patterns across different cancer types by comparing tumor vs. normal and clinical subgroups (e.g. tumor stage, hyper-mutator status, and MSI designation). Interesting observations include striking splice variations in APP, the amyloid beta precursor protein, and in PKM, which is centrally involved in the balance between oxidative and glycolytic metabolism in tumors. SpliceSeq is freely available at http://bioinformatics.mdanderson.org/main/SpliceSeq:Overview. 1. Ryan MC, Cleland J, Kim R, Wong WC, Weinstein JN. SpliceSeq: A Resource for Analysis and Visualization of RNA-Seq Data on Alternative Splicing and Its Functional Impacts. Bioinformatics, 10.1093, 2012. 2. Ryan MC, Zeeberg BR, Caplen NJ, Cleland JA, Kahn AB, Liu H, and Weinstein JN. SpliceCenter: a suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies. BMC Bioinformatics 9:313, 2008. 3. Kahn AB, Ryan MC, Liu H, Zeeberg BR, Jamison DC, Weinstein JN. SpliceMiner: a high-throughput database implementation of the NCBI Evidence Viewer for microarray splice variant analysis. BMC Bioinformatics 8:752, 2006. Supported by NCI Grant No. U24CA143883. The content is solely the responsibility of the authors and does not necessarily represent official views of NCI or NIH. Also supported in part by a gift from the Mary K. Chapman Foundation and a grant from the Michael and Susan Dell Foundation honoring Lorraine Dell. Citation Format: Michael C. Ryan, John N. Weinstein. Analysis of TCGA RNASeq data using SpliceSeq provides a survey of alternative splicing in cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1796. doi:10.1158/1538-7445.AM2013-1796

With the development of high-throughput techniques, RNA-sequencing (RNA-seq) is becoming increasingly popular as an alternative for gene expression analysis, such as RNAs profiling and classification. Identifying which type of diseases a new patient belongs to with RNA-seq data has been recognized as a vital problem in medical research. As RNA-seq data are discrete, statistical methods developed for classifying microarray data cannot be readily applied for RNA-seq data classification. Witten proposed a Poisson linear discriminant analysis (PLDA) to classify the RNA-seq data in 2011. Note, however, that the count datasets are frequently characterized by excess zeros in real RNA-seq or microRNA sequence data (i.e. when the sequence depth is not enough or small RNAs with the length of 18-30 nucleotides). Therefore, it is desired to develop a new model to analyze RNA-seq data with an excess of zeros. In this paper, we propose a Zero-Inflated Poisson Logistic Discriminant Analysis (ZIPLDA) for RNA-seq data with an excess of zeros. The new method assumes that the data are from a mixture of two distributions: one is a point mass at zero, and the other follows a Poisson distribution. We then consider a logistic relation between the probability of observing zeros and the mean of the genes and the sequencing depth in the model. Simulation studies show that the proposed method performs better than, or at least as well as, the existing methods in a wide range of settings. Two real datasets including a breast cancer RNA-seq dataset and a microRNA-seq dataset are also analyzed, and they coincide with the simulation results that our proposed method outperforms the existing competitors. The software is available at http://www.math.hkbu.edu.hk/∼tongt. xwan@comp.hkbu.edu.hk or tongt@hkbu.edu.hk. Supplementary data are available at Bioinformatics online.

The rate of raw sequence production through Next-Generation Sequencing (NGS) has been growing exponentially due to improved technology and reduced costs. This has enabled researchers to answer many biological questions through “multi-omics” data analyses. Even though such data promises new insights into how biological systems function and understanding disease mechanisms, computational analyses performed on such large datasets comes with its challenges and potential pitfalls. The aim of this study was to develop a robust portable and reproducible bioinformatic pipeline for the automation of RNA sequencing (RNA-seq) data analyses. Using Nextflow as a workflow management system and Singularity for application containerisation, the nf-rnaSeqCount pipeline was developed for mapping raw RNA-seq reads to a reference genome and quantifying abundance of identified genomic features for differential gene expression analyses. The pipeline provides a quick and efficient way to obtain a matrix of read counts that can be used with tools such as DESeq2 and edgeR for differential expression analysis. Robust and flexible bioinformatic and computational pipelines for RNA-seq data analysis, from QC to sequence alignment and comparative analyses, will reduce analysis time, and increase accuracy and reproducibility of findings to promote transcriptome research.

Transcriptome sequencing (mRNA-seq) is becoming a very versatile technique for profiling tumors, extending beyond its original intent of transcript quantification, identification of alternative transcripts, and detection of gene fusions. For instance, through recent advancements in RNA-seq data analyses, one can now computationally assess allele-specific gene expression and generate profiles of expressed somatic mutations. Here, we demonstrate the ability to identify chromosomal allelic imbalances (AI) through detection of haplotype-specific patterns in gene transcripts. This class of RNA-based observations may potentially reveal DNA-level chromosomal allelic imbalances or uncover large regions of transcription deregulation. From the TCGA Uterine Carcinosarcoma project, we downloaded exome and RNA sequencing data for 48 patients’ tumor/normal sample pairs in addition to their clinical annotations. We also downloaded Affymetrix SNP6-based DNA copy number event calls made by the TCGA for use as a gold standard when evaluating the AI calls in the exome and RNA-seq. AI calls were made in both the exome and RNA-seq data by: (1) calling 1000 Genomes genotypes in the sequencing data, (2) phasing haplotypes and then (3) characterizing haplotype imbalances using a tool that we developed called hapLOHseq. hapLOHseq applied to the exome and RNA-seq data both resulted in a 72% specificity for identifying the gold standard AI events. In RNA-seq data we detected 43% of the chromosomal AI events identified in the exome sequencing data. When considering AI events specifically detected in the RNA-seq and not the gold standard (RNA-specific AI), the data suggest that higher RNA-specific AI loads could be negatively associated with survival (p-val = 0.076), with higher RNA-specific AI load patients having a median survival of 771 days compared to 1526 days for those patients with lower loads of RNA-specific AI. In conclusion, our results suggest that analysis of chromosomal AI in RNA-seq has equal specificity for detecting DNA-level AI when compared to exome sequencing, although at lower sensitivities. Clinically, our analyses suggest that patients with higher RNA-specific AI load may have a worse overall survival prognosis. The AI we are identifying in the RNA-seq samples may reflect large-scale transcription defects, resulting in a negative impact on the survival of patients. One possible cause of RNA-specific allelic imbalance could be the presence of cis mutations that impact a large-region of the transcription of one of the two haplotypes. Currently, we are identifying areas for improvement in our analytical methods, while interrogating and characterizing exome and RNA-seq AI in additional data sets. Citation Format: Francis A. San Lucas, Yihua Liu, Zachary Weber, Erik Ehli, Gareth Davies, Paul Scheet. Characterization of chromosomal allelic imbalances through RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3572. doi:10.1158/1538-7445.AM2017-3572

BackgroundWheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is an important disease of barley and wheat. A diverse sexual Pgt population from the Pacific Northwest (PNW) region of the US contains a high proportion of individuals with virulence on the barley stem rust resistance (R) gene, Rpg1. However, the evolutionary mechanisms of this virulence on Rpg1 are mysterious considering that Rpg1 had not been deployed in the region and the gene had remained remarkably durable in the Midwestern US and prairie provinces of Canada.Methods and resultsTo identify AvrRpg1 effectors, genome wide association studies (GWAS) were performed using 113 Pgt isolates collected from the PNW (n = 89 isolates) and Midwest (n = 24 isolates) regions of the US. Disease phenotype data were generated on two barley lines Morex and the Golden Promise transgenic (H228.2c) that carry the Rpg1 gene. Genotype data was generated by whole genome sequencing (WGS) of 96 isolates (PNW = 89 isolates and Midwest = 7 isolates) and RNA sequencing (RNAseq) data from 17 Midwestern isolates. Utilizing ~1.2 million SNPs generated from WGS and phenotype data (n = 96 isolates) on the transgenic line H228.2c, 53 marker trait associations (MTAs) were identified. Utilizing ~140 K common SNPs generated from combined analysis of WGS and RNAseq data, two significant MTAs were identified using the cv Morex phenotyping data. The 55 MTAs defined two distinct avirulence loci, on supercontig 2.30 and supercontig 2.11 of the Pgt reference genome of Pgt isolate CRL 75-36-700-3. The major avirulence locus designated AvrRpg1A was identified with the GWAS using both barley lines and was delimited to a 35 kb interval on supercontig 2.30 containing four candidate genes (PGTG_10878, PGTG_10884, PGTG_10885, and PGTG_10886). The minor avirulence locus designated AvrRpg1B identified with cv Morex contained a single candidate gene (PGTG_05433). AvrRpg1A haplotype analysis provided strong evidence that a dominant avirulence gene underlies the locus.ConclusionsThe association analysis identified strong candidate AvrRpg1 genes. Further analysis to validate the AvrRpg1 genes will fill knowledge gaps in our understanding of rust effector biology and the evolution and mechanism/s of Pgt virulence on Rpg1.

p53 is inactivated in approximately half of human cancers. p53 execute various functions by modulating transcriptional regulation. To identify direct p53 transcriptional targets including non-coding RNAs, we performed gene expression analysis with next-generation sequencing (RNA-seq) in osteosarcoma U2OS cells treated with Nutlin-3a which activates endogenous p53. We identified 261 genes increased more than 4-fold and 373 long non-coding RNAs (lncRNAs) increased more than 2-fold. Next, we analyzed the data of chromatin immunoprecipitation with next-generation sequencing (ChIP-seq) and then searched for p53 binding sites in the whole human genome. p53 binding sites were located in the neighborhood of 126/261 (48.3%) genes including 28 known target genes and 122/373 (32.7%) lncRNAs including 7 lncRNAs we already identified in previous study. Recent reports have revealed that lncRNAs play an important role in various biological and pathological processes such as development, differentiation, stemness and carcinogenesis. Although all functions of lncRNAs have not been elucidated, many lncRNAs are involved in transcriptional regulation like transcriptional factors. Therefore, we performed a gene network analysis of cancer transcriptome including lncRNAs using public RNA-seq data of cancer tissues. As a result, we identified several hub lncRNAs which regulate the transcription of many downstream genes. Our results suggest that p53 and lncRNAs comprise a complex transcriptional network for various biological functions and tumor suppression. Citation Format: Masashi Idogawa, Tomoko Ohashi, Yasushi Sasaki, Takashi Tokino. The identification of p53 target genes and noncoding RNAs through the combined analysis of RNA-seq and ChIP-seq data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1938.

BackgroundRNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement.ResultsMotivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species.ConclusionsRNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

<p>Table_S1. Study cohort. Table_S2. The sample composition of each dataset. Table_S3. Patient information. Table_S4. Whole genome sequencing (WGS) datasets. 1,411 UC whole genomes. Table_S5. Whole exome sequencing (WES) datasets. Table_S6. RNA sequencing (RNA-seq) datasets. Table_S7. Circle-seq datasets. Table_S8. Single-cell RNA-seq (scRNA-seq) datasets. Table_S9. Amplicon classification. Table_S10. Amplicon similarity scores. Table_S11. ecDNA prevalence in human cancers. Table_S12. Genomic and sequence features of amplicons. Table_S13. Oncogene list. Table_S14. Genes encoded on all focal amplifications. Table_S15. ecDNA status and CNV ITH in tumor multi-region sampling cohort. Table_S16. Diver gene alterations in ecDNA- and ecDNA+ tumors. Table_S17. Summary of Single-cell RNA-seq (scRNA-seq) data. Table_S18. IHC evaluation of CD45 in 19 tumors with scRNA-seq data. Table_S19. Signature genes of 50 robust NMF programs. Table_S20. Pathway enriched in each NMF program. Table_S21. Top 30 signature genes of tumor meta-program. Table_S22. Differential pathways of malignant cells between ecDNA- and ecDNA+ tumors. Table_S23. Gene signature matrix for deconvolution analysis of bulk RNA-seq data. Table_S24. Software.</p>

High-quality RNA is crucial for obtaining reliable RNA sequencing (RNA-seq) data, with metrics like RNA Integrity Number (RIN). However, these metrics, while effective for evaluating RNA integrity, do not always correlate with RNA-seq data quality, especially at the transcript level. This gap is particularly evident in total RNA-seq, where existing measures such as the coefficient of variation (CV) for read coverage fail to fully capture data quality and are influenced by confounding factors like read coverage depth. To address this, we developed a novel method to assess RNA-seq data quality by quantifying nonuniformity in read coverage while minimizing the influence of read coverage depth. In this study we invented new matric called windowCV (wCV) and applied to a diverse range of RNA-seq datasets, including fresh frozen (FF) and FFPE total RNA-seq data, as well as poly(A)-enriched mRNA-seq data. In mRNA-seq, our method captured 3' read coverage bias, a hallmark of RNA degradation, particularly in longer transcripts. For total RNA-seq data, we identified noisy coverage patterns associated with poor data quality, even in samples with high RIN values. By fitting regression lines between wCV and mean coverage depth (MCD) and calculating the area under the curve (wCVAUC), we refined the assessment to account for RNA quality variability. Using the TCGA pilot study and our own datasets, we demonstrated that wCVAUC reliably identified low quality RNA-seq data and highlighted its impact on downstream analyses, including gene expression quantification and clustering. Importantly, we observed that low RIN values do not always predict poor RNA-seq data quality, as some samples with RIN values below 7 exhibited high-quality RNA-seq data based on wCVAUC. It means that our analyses showed that wCVAUC effectively distinguished high-quality from low-quality samples, including cases where traditional metrics like RIN and CV were insufficient. Additionally, our investigation into the relationships between nonuniformity of read coverage, exon GC content, and RNA localization revealed that the transcript-level RNA-seq data quality of lncRNA genes in FFPE samples is influenced by low exon GC content and nuclear localization. In conclusion, our method provides robust, transcript-level metrics for assessing RNA-seq data quality across platforms, enabling more accurate identification of low-quality data and minimizing biases in downstream analyses. This approach offers a new standard for integrating RNA-seq data quality with sample variability, particularly for challenging datasets such as FFPE and total RNA-seq. Citation Format: Wonyoung Choi, Miyeon Yeon, Jay Lee, Hyo Young Choi, David Neil Hayes. The new approach for measuring nonuniformity of read coverages reveals the quality of RNA-seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2489.

BackgroundHigh throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here we apply robust statistics on RNA-seq data analysis.ResultsWe report the use of two robust principal component analysis (rPCA) methods, PcaHubert and PcaGrid, to detect outlier samples in multiple simulated and real biological RNA-seq data sets with positive control outlier samples. PcaGrid achieved 100% sensitivity and 100% specificity in all the tests using positive control outliers with varying degrees of divergence. We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granule layer in the cerebellum of control and conditional SnoN knockout mice. Both rPCA methods detected the same two outlier samples but cPCA failed to detect any. We performed differentially expressed gene detection before and after outlier removal as well as with and without batch effect modeling. We validated gene expression changes using quantitative reverse transcription PCR and used the result as reference to compare the performance of eight different data analysis strategies. Removing outliers without batch effect modeling performed the best in term of detecting biologically relevant differentially expressed genes.ConclusionsrPCA implemented in the PcaGrid function is an accurate and objective method to detect outlier samples. It is well suited for high-dimensional data with small sample sizes like RNA-seq data. Outlier removal can significantly improve the performance of differential gene detection and downstream functional analysis.

BackgroundMaize, a crop of global significance, is vulnerable to a variety of biotic stresses resulting in economic losses. Fusarium verticillioides (teleomorph Gibberella moniliformis) is one of the key fungal pathogens of maize, causing ear rots and stalk rots. To better understand the genetic mechanisms involved in maize defense as well as F. verticillioides virulence, a systematic investigation of the host-pathogen interaction is needed. The aim of this study was to computationally identify potential maize subnetwork modules associated with its defense response against F. verticillioides.ResultsWe obtained time-course RNA-seq data from B73 maize inoculated with wild type F. verticillioides and a loss-of-virulence mutant, and subsequently established a computational pipeline for network-based comparative analysis.Specifically, we first analyzed the RNA-seq data by a cointegration-correlation-expression approach, where maize genes were jointly analyzed with known F. verticillioides virulence genes to find candidate maize genes likely associated with the defense mechanism. We predicted maize co-expression networks around the selected maize candidate genes based on partial correlation, and subsequently searched for subnetwork modules that were differentially activated when inoculated with two different fungal strains. Based on our analysis pipeline, we identified four potential maize defense subnetwork modules. Two were directly associated with maize defense response and were associated with significant GO terms such as GO:0009817 (defense response to fungus) and GO:0009620 (response to fungus). The other two predicted modules were indirectly involved in the defense response, where the most significant GO terms associated with these modules were GO:0046914 (transition metal ion binding) and GO:0046686 (response to cadmium ion).ConclusionThrough our RNA-seq data analysis, we have shown that a network-based approach can enhance our understanding of the complicated host-pathogen interactions between maize and F. verticillioides by interpreting the transcriptome data in a system-oriented manner. We expect that the proposed analytic pipeline can also be adapted for investigating potential functional modules associated with host defense response in diverse plant-pathogen interactions.

The number of studies dealing with RNA-Seq data analysis has experienced a fast increase in the past years making this type of gene expression a strong competitor to the DNA microarrays. This paper proposes a Bayesian model to detect low and highly-expressed chromosome regions using RNA-Seq data. The methodology is based on a recent work designed to detect highly-expressed (overexpressed) regions in the context of microarray data. A hidden Markov model is developed by considering a mixture of Gaussian distributions with ordered means in a way that first and last mixture components are supposed to accommodate the under and overexpressed genes, respectively. The model is flexible enough to efficiently deal with the highly irregular spaced configuration of the data by assuming a hierarchical Markov dependence structure. The analysis of four cancer data sets (breast, lung, ovarian and uterus) is presented. Results indicate that the proposed model is selective in determining the expression status, robust with respect to prior specifications and provides tools for a global or local search of under and overexpressed chromosome regions.